A Clinician's Guide to Fairness in Healthcare AI from Development to Monitoring

Fairness in healthcare AI is not a one-time check but a continuous responsibility spanning the entire lifecycle—from problem formulation to postdeployment monitoring. Clinicians must recognize biases like shortcut learning, distribution shift, and automation bias, and demand sub…

• The review identifies red flags and mitigation strategies for each of six lifecycle stages: problem formulation, data generation, model development, evaluation, implementation, and postdeployment monitoring.
• Key sources of unfairness include biased proxy outcomes, unrepresentative data, hidden stratification, distribution shift, and human-AI interaction effects such as automation bias.
• Sustained fairness requires transparent design, subgroup-stratified evaluations, external validation, local governance, and continuous monitoring across diverse populations and settings.

Automated summary

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